{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4dc1bf63",
   "metadata": {},
   "source": [
    "# 08-Difference-in-Differences\n",
    "\n",
    "\n",
    "## Panel Data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a6d9bc21",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-01-20T14:39:09.552555Z",
     "start_time": "2024-01-20T14:39:06.893550Z"
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "from toolz import *\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import statsmodels.formula.api as smf\n",
    "\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt\n",
    "import matplotlib\n",
    "\n",
    "from cycler import cycler\n",
    "\n",
    "color=['0.0', '0.4', '0.8']\n",
    "default_cycler = (cycler(color=color))\n",
    "linestyle=['-', '--', ':', '-.']\n",
    "marker=['o', 'v', 'd', 'p']\n",
    "\n",
    "plt.rc('axes', prop_cycle=default_cycler)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d17c7963",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-01-20T14:39:09.589128Z",
     "start_time": "2024-01-20T14:39:09.554340Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>city</th>\n",
       "      <th>region</th>\n",
       "      <th>treated</th>\n",
       "      <th>tau</th>\n",
       "      <th>downloads</th>\n",
       "      <th>post</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-05-01</td>\n",
       "      <td>5</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-05-02</td>\n",
       "      <td>5</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-05-03</td>\n",
       "      <td>5</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-05-04</td>\n",
       "      <td>5</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-05-05</td>\n",
       "      <td>5</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date  city region  treated  tau  downloads  post\n",
       "0 2021-05-01     5      S        0  0.0       51.0     0\n",
       "1 2021-05-02     5      S        0  0.0       51.0     0\n",
       "2 2021-05-03     5      S        0  0.0       51.0     0\n",
       "3 2021-05-04     5      S        0  0.0       50.0     0\n",
       "4 2021-05-05     5      S        0  0.0       49.0     0"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "mkt_data = (pd.read_csv(\"./data/short_offline_mkt_south.csv\")\n",
    "            .astype({\"date\":\"datetime64[ns]\"}))\n",
    "\n",
    "mkt_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3d6cdba1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-01-20T14:39:09.608185Z",
     "start_time": "2024-01-20T14:39:09.594150Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">date</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-05-01</td>\n",
       "      <td>2021-06-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-05-15</td>\n",
       "      <td>2021-06-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date           \n",
       "         min        max\n",
       "w                      \n",
       "0 2021-05-01 2021-06-01\n",
       "1 2021-05-15 2021-06-01"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(mkt_data\n",
    " .assign(w = lambda d: d[\"treated\"]*d[\"post\"])\n",
    " .groupby([\"w\"])\n",
    " .agg({\"date\":[min, max]}))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43105f6e",
   "metadata": {},
   "source": [
    "## Canonical Difference-in-Differences\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "355d9c2c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-01-20T14:39:09.622154Z",
     "start_time": "2024-01-20T14:39:09.611867Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>downloads</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>treated</th>\n",
       "      <th>post</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">0</th>\n",
       "      <th>0</th>\n",
       "      <td>50.335034</td>\n",
       "      <td>2021-05-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50.556878</td>\n",
       "      <td>2021-05-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1</th>\n",
       "      <th>0</th>\n",
       "      <td>50.944444</td>\n",
       "      <td>2021-05-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>51.858025</td>\n",
       "      <td>2021-05-15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              downloads       date\n",
       "treated post                      \n",
       "0       0     50.335034 2021-05-01\n",
       "        1     50.556878 2021-05-15\n",
       "1       0     50.944444 2021-05-01\n",
       "        1     51.858025 2021-05-15"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "did_data = (mkt_data\n",
    "            .groupby([\"treated\", \"post\"])\n",
    "            .agg({\"downloads\":\"mean\", \"date\": \"min\"}))\n",
    "\n",
    "did_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "79bcb7fe",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-01-20T14:39:09.632122Z",
     "start_time": "2024-01-20T14:39:09.624048Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6917359536407233"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y0_est = (did_data.loc[1].loc[0, \"downloads\"] # treated baseline\n",
    "          # control evolution\n",
    "          + did_data.loc[0].diff().loc[1, \"downloads\"]) \n",
    "\n",
    "att = did_data.loc[1].loc[1, \"downloads\"] - y0_est\n",
    "att"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b51d6822",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-01-20T14:39:09.640871Z",
     "start_time": "2024-01-20T14:39:09.633597Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7660316402518457"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mkt_data.query(\"post==1\").query(\"treated==1\")[\"tau\"].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e911d8b1",
   "metadata": {},
   "source": [
    "### Diff-in-Diff with Outcome Growth\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "10e16d8d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-01-20T14:39:09.656244Z",
     "start_time": "2024-01-20T14:39:09.642523Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>delta_y</th>\n",
       "      <th>treated</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>0.555556</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>0.166667</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>0.420635</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>0.119048</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>1.595238</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       delta_y  treated\n",
       "city                   \n",
       "192   0.555556        0\n",
       "193   0.166667        0\n",
       "195   0.420635        0\n",
       "196   0.119048        0\n",
       "197   1.595238        1"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pre = mkt_data.query(\"post==0\").groupby(\"city\")[\"downloads\"].mean()\n",
    "post = mkt_data.query(\"post==1\").groupby(\"city\")[\"downloads\"].mean()\n",
    "\n",
    "delta_y = ((post - pre)\n",
    "           .rename(\"delta_y\")\n",
    "           .to_frame()\n",
    "           # add the treatment dummy\n",
    "           .join(mkt_data.groupby(\"city\")[\"treated\"].max()))\n",
    "\n",
    "delta_y.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "31be6542",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-01-20T14:39:09.665299Z",
     "start_time": "2024-01-20T14:39:09.659783Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6917359536407155"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(delta_y.query(\"treated==1\")[\"delta_y\"].mean() \n",
    " - delta_y.query(\"treated==0\")[\"delta_y\"].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ff0261df",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-01-20T14:39:09.877683Z",
     "start_time": "2024-01-20T14:39:09.666729Z"
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f960ada4dd0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "did_plt = did_data.reset_index()\n",
    "\n",
    "\n",
    "plt.figure(figsize=(10,4))\n",
    "\n",
    "sns.scatterplot(data=did_plt.query(\"treated==0\"), x=\"date\", y=\"downloads\", s=100, color=\"C0\", marker=\"s\")\n",
    "sns.lineplot(data=did_plt.query(\"treated==0\"), x=\"date\", y=\"downloads\", label=\"Control\", color=\"C0\")\n",
    "\n",
    "sns.scatterplot(data=did_plt.query(\"treated==1\"), x=\"date\", y=\"downloads\", s=100, color=\"C1\", marker=\"x\")\n",
    "sns.lineplot(data=did_plt.query(\"treated==1\"), x=\"date\", y=\"downloads\", label=\"Treated\", color=\"C1\",)\n",
    "\n",
    "plt.plot(did_data.loc[1, \"date\"], [did_data.loc[1, \"downloads\"][0], y0_est], color=\"C2\", linestyle=\"dashed\", label=\"Y(0)|D=1\")\n",
    "plt.scatter(did_data.loc[1, \"date\"], [did_data.loc[1, \"downloads\"][0], y0_est], color=\"C2\", s=50)\n",
    "\n",
    "plt.xticks(rotation = 45);\n",
    "plt.legend()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d388983",
   "metadata": {},
   "source": [
    "### Diff-in-Diff with OLS\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "bead249f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-01-20T14:39:09.887639Z",
     "start_time": "2024-01-20T14:39:09.879115Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>post</th>\n",
       "      <th>downloads</th>\n",
       "      <th>date</th>\n",
       "      <th>treated</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>50.642857</td>\n",
       "      <td>2021-05-01</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>50.166667</td>\n",
       "      <td>2021-05-15</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>49.142857</td>\n",
       "      <td>2021-05-01</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>49.166667</td>\n",
       "      <td>2021-05-15</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>48.785714</td>\n",
       "      <td>2021-05-01</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   city  post  downloads       date  treated\n",
       "0     5     0  50.642857 2021-05-01        0\n",
       "1     5     1  50.166667 2021-05-15        0\n",
       "2    15     0  49.142857 2021-05-01        0\n",
       "3    15     1  49.166667 2021-05-15        0\n",
       "4    20     0  48.785714 2021-05-01        0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "did_data = (mkt_data\n",
    "            .groupby([\"city\", \"post\"])\n",
    "            .agg({\"downloads\":\"mean\", \"date\": \"min\", \"treated\": \"max\"})\n",
    "            .reset_index())\n",
    "\n",
    "did_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "54757217",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-01-20T14:39:09.896935Z",
     "start_time": "2024-01-20T14:39:09.889021Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6917359536406904"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import statsmodels.formula.api as smf\n",
    "\n",
    "smf.ols(\n",
    "    'downloads ~ treated*post', data=did_data\n",
    ").fit().params[\"treated:post\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b379c13",
   "metadata": {},
   "source": [
    "### Diff-in-Diff with Fixed Effects\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "bd379d42",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-01-20T14:39:09.913703Z",
     "start_time": "2024-01-20T14:39:09.898238Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6917359536407091"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m = smf.ols('downloads ~ treated:post + C(city) + C(post)',\n",
    "            data=did_data).fit()\n",
    "\n",
    "m.params[\"treated:post\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48ef6933",
   "metadata": {},
   "source": [
    "### Multiple Time Periods\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d1eed2c4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-01-20T14:39:11.143922Z",
     "start_time": "2024-01-20T14:39:09.915271Z"
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.ticker as plticker\n",
    "\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(2,1, figsize=(9, 12), sharex=True)\n",
    "\n",
    "heat_plt = (mkt_data\n",
    "            .assign(treated=lambda d: d.groupby(\"city\")[\"treated\"].transform(max))            \n",
    "            .astype({\"date\":\"str\"})\n",
    "            .assign(treated=mkt_data[\"treated\"]*mkt_data[\"post\"])\n",
    "            .pivot(\"city\", \"date\", \"treated\")\n",
    "            .reset_index()\n",
    "            .sort_values(max(mkt_data[\"date\"].astype(str)), ascending=False)\n",
    "            .reset_index()\n",
    "            .drop(columns=[\"city\"])\n",
    "            .rename(columns={\"index\":\"city\"})\n",
    "            .set_index(\"city\"))\n",
    "\n",
    "\n",
    "sns.heatmap(heat_plt, cmap=\"gray\", linewidths=0.01, linecolor=\"0.5\", ax=ax1, cbar=False)\n",
    "\n",
    "ax1.set_title(\"Treatment Assignment\")\n",
    "\n",
    "\n",
    "sns.lineplot(data=mkt_data.astype({\"date\":\"str\"}),\n",
    "             x=\"date\", y=\"downloads\", hue=\"treated\", ax=ax2)\n",
    "\n",
    "loc = plticker.MultipleLocator(base=2.0)\n",
    "# ax2.xaxis.set_major_locator(loc)\n",
    "ax2.vlines(\"2021-05-15\", mkt_data[\"downloads\"].min(), mkt_data[\"downloads\"].max(), color=\"black\", ls=\"dashed\", label=\"Interv.\")\n",
    "ax2.set_title(\"Outcome Over Time\")\n",
    "\n",
    "plt.xticks(rotation = 50);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "647b569a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-01-20T14:39:11.154791Z",
     "start_time": "2024-01-20T14:39:11.145888Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6917359536407226"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m = smf.ols('downloads ~ treated*post', data=mkt_data).fit()\n",
    "\n",
    "m.params[\"treated:post\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a60e4fdc",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-01-20T14:39:11.208103Z",
     "start_time": "2024-01-20T14:39:11.156607Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6917359536407017"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m = smf.ols('downloads ~ treated:post + C(city) + C(date)',\n",
    "            data=mkt_data).fit()\n",
    "\n",
    "m.params[\"treated:post\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55619cf5",
   "metadata": {},
   "source": [
    "### Inference\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7d2cd163",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-01-20T14:39:11.263156Z",
     "start_time": "2024-01-20T14:39:11.209780Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ATT: 0.6917359536407017\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0    0.296101\n",
       "1    1.087370\n",
       "Name: treated:post, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m = smf.ols(\n",
    "    'downloads ~ treated:post + C(city) + C(date)', data=mkt_data\n",
    ").fit(cov_type='cluster', cov_kwds={'groups': mkt_data['city']})\n",
    "\n",
    "print(\"ATT:\", m.params[\"treated:post\"])\n",
    "m.conf_int().loc[\"treated:post\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e0d03f38",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-01-20T14:39:11.313615Z",
     "start_time": "2024-01-20T14:39:11.265201Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ATT: 0.6917359536407017\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0    0.478014\n",
       "1    0.905457\n",
       "Name: treated:post, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m = smf.ols('downloads ~ treated:post + C(city) + C(date)',\n",
    "            data=mkt_data).fit()\n",
    "\n",
    "print(\"ATT:\", m.params[\"treated:post\"])\n",
    "m.conf_int().loc[\"treated:post\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "05dd249f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-01-20T14:39:11.329426Z",
     "start_time": "2024-01-20T14:39:11.315450Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ATT: 0.6917359536407091\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0    0.138188\n",
       "1    1.245284\n",
       "Name: treated:post, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m = smf.ols(\n",
    "    'downloads ~ treated:post + C(city) + C(date)', data=did_data\n",
    ").fit(cov_type='cluster', cov_kwds={'groups': did_data['city']})\n",
    "\n",
    "print(\"ATT:\", m.params[\"treated:post\"])\n",
    "m.conf_int().loc[\"treated:post\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b7b5b5bd",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-01-20T14:39:11.333852Z",
     "start_time": "2024-01-20T14:39:11.331302Z"
    }
   },
   "outputs": [],
   "source": [
    "def block_sample(df, unit_col):\n",
    "    \n",
    "    units = df[unit_col].unique()\n",
    "    sample = np.random.choice(units, size=len(units), replace=True) \n",
    "    \n",
    "    return (df\n",
    "            .set_index(unit_col)\n",
    "            .loc[sample]\n",
    "            .reset_index(level=[unit_col]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b6fb9979",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-01-20T14:39:11.387119Z",
     "start_time": "2024-01-20T14:39:11.335017Z"
    }
   },
   "outputs": [],
   "source": [
    "from joblib import Parallel, delayed\n",
    "\n",
    "def block_bootstrap(data, est_fn, unit_col,\n",
    "                    rounds=200, seed=123, pcts=[2.5, 97.5]):\n",
    "    np.random.seed(seed)\n",
    "    \n",
    "    stats = Parallel(n_jobs=4)(\n",
    "        delayed(est_fn)(block_sample(data, unit_col=unit_col))\n",
    "        for _ in range(rounds))\n",
    "    \n",
    "    return np.percentile(stats, pcts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "95c7a1b3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-01-20T14:39:14.534420Z",
     "start_time": "2024-01-20T14:39:11.388302Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.23162214, 1.14002646])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def est_fn(df):\n",
    "    m = smf.ols('downloads ~ treated:post + C(city) + C(date)',\n",
    "                data=df).fit()\n",
    "    return m.params[\"treated:post\"]\n",
    "\n",
    "block_bootstrap(mkt_data, est_fn, \"city\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9488a76d",
   "metadata": {},
   "source": [
    "## Identification Assumptions\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3f34a6a",
   "metadata": {},
   "source": [
    "### Parallel Trends\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "708b214a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-01-20T14:39:14.882883Z",
     "start_time": "2024-01-20T14:39:14.535841Z"
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Log(y)')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x504 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2, ax3)  = plt.subplots(1, 3, figsize=(20,7))\n",
    "\n",
    "obs_df = pd.DataFrame(dict(\n",
    "    period = [2,3, 2,3],\n",
    "    treated = [0,0, 1,1],\n",
    "    y = [4,6, 10,16],\n",
    "))\n",
    "\n",
    "baseline = 10-4\n",
    "\n",
    "\n",
    "plt_d1 = pd.DataFrame(dict(\n",
    "    period = [1,2,3,4, 1,2,3,4],\n",
    "    treated = [0,0,0,0, 1,1,1,1],\n",
    "    y = [2,4,6,8, 8,10,12,14],\n",
    "))\n",
    "\n",
    "sns.lineplot(data=plt_d1, x=\"period\", y=\"y\", hue=\"treated\", linestyle=\"dashed\", legend=None, ax=ax1)\n",
    "sns.lineplot(data=obs_df, x=\"period\", y=\"y\", hue=\"treated\", legend=None, ax=ax1)\n",
    "sns.lineplot(data=obs_df.assign(y=obs_df[\"y\"] + baseline).query(\"treated==0\"),\n",
    "             x=\"period\", y=\"y\",  legend=None, ax=ax1, color=\"C0\", linestyle=\"dotted\")\n",
    "\n",
    "sns.scatterplot(data=obs_df, x=\"period\", y=\"y\", hue=\"treated\", style=\"treated\", s=100, ax=ax1)\n",
    "ax1.set_title(\"Parallel Trends\")\n",
    "\n",
    "\n",
    "plt_d2 = pd.DataFrame(dict(\n",
    "    period = [1,2,3,4, 1,2,3,4],\n",
    "    treated = [0,0,0,0, 1,1,1,1],\n",
    "    y = [2,4,6,8, 9,10,11,12],\n",
    "))\n",
    "\n",
    "sns.lineplot(data=plt_d2, x=\"period\", y=\"y\", hue=\"treated\", linestyle=\"dashed\", legend=None, ax=ax2)\n",
    "sns.lineplot(data=obs_df, x=\"period\", y=\"y\", hue=\"treated\", legend=None, ax=ax2)\n",
    "sns.scatterplot(data=obs_df, x=\"period\", y=\"y\", hue=\"treated\", style=\"treated\", s=100, ax=ax2)\n",
    "sns.lineplot(data=obs_df.assign(y=obs_df[\"y\"] + baseline).query(\"treated==0\"),\n",
    "             x=\"period\", y=\"y\",  legend=None, ax=ax2, color=\"C0\", linestyle=\"dotted\")\n",
    "\n",
    "ax2.set_title(\"Diverging Trends\")\n",
    "\n",
    "\n",
    "\n",
    "non_lin = np.log\n",
    "non_lin_obs = obs_df.assign(y=non_lin(obs_df[\"y\"]))\n",
    "\n",
    "plt_d3 = pd.DataFrame(dict(\n",
    "    period = [1,2,3,4, 1,2,3,4],\n",
    "    treated = [0,0,0,0, 1,1,1,1],\n",
    "    y = non_lin([2,4,6,8, 8,10,12,14]),\n",
    "))\n",
    "\n",
    "\n",
    "sns.lineplot(data=plt_d3, x=\"period\", y=\"y\", hue=\"treated\", linestyle=\"dashed\", legend=None, ax=ax3)\n",
    "sns.lineplot(data=non_lin_obs, x=\"period\", y=\"y\", hue=\"treated\", legend=None, ax=ax3)\n",
    "sns.scatterplot(data=non_lin_obs, x=\"period\", y=\"y\", hue=\"treated\", style=\"treated\", s=100, ax=ax3)\n",
    "sns.lineplot(x=[2, 3], y=non_lin_obs.query(\"treated==1 & period==2\")[\"y\"].values - non_lin_obs.query(\"treated==0 & period==2\")[\"y\"].values + non_lin_obs.query(\"treated==0\")[\"y\"], color=\"C0\", \n",
    "            linestyle=\"dotted\")\n",
    "\n",
    "\n",
    "ax3.set_title(\"Log Scale\")\n",
    "ax3.set_ylabel(\"Log(y)\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6cb74f2b",
   "metadata": {},
   "source": [
    "### No Anticipation Assumption and SUTVA \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4eba4765",
   "metadata": {},
   "source": [
    "### Strict Exogeneity\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6967f77b",
   "metadata": {},
   "source": [
    "### No Time Varying Confounders\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2ff9a0d",
   "metadata": {},
   "source": [
    "### No Feedback\n",
    "\n",
    "### No Carryover and no Lagged Dependent Variable\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "112bc864",
   "metadata": {},
   "source": [
    "## Effect Dynamics Over Time\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "3671f124",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-01-20T14:39:14.887265Z",
     "start_time": "2024-01-20T14:39:14.884119Z"
    }
   },
   "outputs": [],
   "source": [
    "def did_date(df, date):\n",
    "    df_date = (df\n",
    "               .query(\"date==@date | post==0\")\n",
    "               .query(\"date <= @date\")\n",
    "               .assign(post = lambda d: (d[\"date\"]==date).astype(int)))\n",
    "    \n",
    "    m = smf.ols(\n",
    "        'downloads ~ I(treated*post) + C(city) + C(date)', data=df_date\n",
    "    ).fit(cov_type='cluster', cov_kwds={'groups': df_date['city']})\n",
    "    \n",
    "    att = m.params[\"I(treated * post)\"]\n",
    "    ci = m.conf_int().loc[\"I(treated * post)\"]\n",
    "    \n",
    "    return pd.DataFrame({\"att\": att, \"ci_low\": ci[0], \"ci_up\": ci[1]},\n",
    "                        index=[date])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "294464f7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-01-20T14:39:15.544541Z",
     "start_time": "2024-01-20T14:39:14.888459Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>att</th>\n",
       "      <th>ci_low</th>\n",
       "      <th>ci_up</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-05-02</th>\n",
       "      <td>0.325397</td>\n",
       "      <td>-0.491741</td>\n",
       "      <td>1.142534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-03</th>\n",
       "      <td>0.384921</td>\n",
       "      <td>-0.388389</td>\n",
       "      <td>1.158231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-04</th>\n",
       "      <td>-0.156085</td>\n",
       "      <td>-1.247491</td>\n",
       "      <td>0.935321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-05</th>\n",
       "      <td>-0.299603</td>\n",
       "      <td>-0.949935</td>\n",
       "      <td>0.350729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-06</th>\n",
       "      <td>0.347619</td>\n",
       "      <td>0.013115</td>\n",
       "      <td>0.682123</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 att    ci_low     ci_up\n",
       "2021-05-02  0.325397 -0.491741  1.142534\n",
       "2021-05-03  0.384921 -0.388389  1.158231\n",
       "2021-05-04 -0.156085 -1.247491  0.935321\n",
       "2021-05-05 -0.299603 -0.949935  0.350729\n",
       "2021-05-06  0.347619  0.013115  0.682123"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "post_dates = sorted(mkt_data[\"date\"].unique())[1:]\n",
    "\n",
    "atts = pd.concat([did_date(mkt_data, date)\n",
    "                  for date in post_dates])\n",
    "\n",
    "atts.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "7e79a77d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-01-20T14:39:15.777294Z",
     "start_time": "2024-01-20T14:39:15.546486Z"
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f96104d1710>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.figure(figsize=(10,4))\n",
    "plt.plot(atts.index, atts[\"att\"], label=\"Est. ATTs\")\n",
    "\n",
    "plt.fill_between(atts.index, atts[\"ci_low\"], atts[\"ci_up\"], alpha=0.1)\n",
    "\n",
    "plt.vlines(pd.to_datetime(\"2021-05-15\"), -2, 3, linestyle=\"dashed\", label=\"intervention\")\n",
    "plt.hlines(0, atts.index.min(), atts.index.max(), linestyle=\"dotted\")\n",
    "\n",
    "plt.plot(atts.index, mkt_data.query(\"treated==1\").groupby(\"date\")[[\"tau\"]].mean().values[1:], color=\"0.6\", ls=\"-.\", label=\"$\\\\tau$\")\n",
    "\n",
    "plt.xticks(rotation=45)\n",
    "plt.title(\"DID ATTs Over Time\")\n",
    "plt.legend()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19419472",
   "metadata": {},
   "source": [
    "## Diff-in-Diff with Covariates\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "772ea9be",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-01-20T14:39:15.793664Z",
     "start_time": "2024-01-20T14:39:15.783819Z"
    }
   },
   "outputs": [],
   "source": [
    "mkt_data_all = (pd.read_csv(\"./data/short_offline_mkt_all_regions.csv\")\n",
    "                .astype({\"date\":\"datetime64[ns]\"}))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "0094ac1b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-01-20T14:39:15.977299Z",
     "start_time": "2024-01-20T14:39:15.794801Z"
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,6))\n",
    "sns.lineplot(data=mkt_data_all.groupby([\"date\", \"region\", \"treated\"])[[\"downloads\"]].mean().reset_index(),\n",
    "             x=\"date\", y=\"downloads\", hue=\"region\", style=\"treated\", palette=\"gray\")\n",
    "\n",
    "plt.vlines(pd.to_datetime(\"2021-05-15\"), 15, 55, ls=\"dotted\", label=\"Intervention\")\n",
    "plt.legend(fontsize=14)\n",
    "\n",
    "plt.xticks(rotation=25);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "60ec5572",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-01-20T14:39:16.361794Z",
     "start_time": "2024-01-20T14:39:15.978681Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True ATT:  1.7208921056102682\n",
      "Estimated ATT: 2.068391984256296\n"
     ]
    }
   ],
   "source": [
    "print(\"True ATT: \", mkt_data_all.query(\"treated*post==1\")[\"tau\"].mean())\n",
    "\n",
    "m = smf.ols('downloads ~ treated:post + C(city) + C(date)',\n",
    "            data=mkt_data_all).fit()\n",
    "\n",
    "print(\"Estimated ATT:\", m.params[\"treated:post\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "e36e4949",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-01-20T14:39:16.809745Z",
     "start_time": "2024-01-20T14:39:16.363503Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.071153674125536"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m = smf.ols('downloads ~ treated:post + C(city) + C(date) + C(region)',\n",
    "            data=mkt_data_all).fit()\n",
    "m.params[\"treated:post\"] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "2cd0d7e7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-01-20T14:39:16.855055Z",
     "start_time": "2024-01-20T14:39:16.811240Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "post:treated                   1.676808\n",
       "post:treated:C(region)[T.N]   -0.343667\n",
       "post:treated:C(region)[T.S]   -0.985072\n",
       "post:treated:C(region)[T.W]    1.369363\n",
       "dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m_saturated = smf.ols('downloads ~ (post*treated)*C(region)',\n",
    "                      data=mkt_data_all).fit()\n",
    "\n",
    "atts = m_saturated.params[m_saturated.params.index.str.contains(\"post:treated\")]\n",
    "atts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "0696dc65",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-01-20T14:39:16.862407Z",
     "start_time": "2024-01-20T14:39:16.857052Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.6940400451471818"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg_size = (mkt_data_all.groupby(\"region\").size()\n",
    "            /len(mkt_data_all[\"date\"].unique()))\n",
    "\n",
    "base = atts[0]\n",
    "\n",
    "np.array([reg_size[0]*base]+\n",
    "         [(att+base)*size\n",
    "          for att, size in zip(atts[1:], reg_size[1:])]\n",
    "        ).sum()/sum(reg_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "babd253f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-01-20T14:39:16.901908Z",
     "start_time": "2024-01-20T14:39:16.864155Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "           <td></td>              <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>           <td>   17.3522</td> <td>    0.101</td> <td>  172.218</td> <td> 0.000</td> <td>   17.155</td> <td>   17.550</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(region)[T.N]</th>      <td>   26.2770</td> <td>    0.137</td> <td>  191.739</td> <td> 0.000</td> <td>   26.008</td> <td>   26.546</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(region)[T.S]</th>      <td>   33.0815</td> <td>    0.135</td> <td>  245.772</td> <td> 0.000</td> <td>   32.818</td> <td>   33.345</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(region)[T.W]</th>      <td>   10.7118</td> <td>    0.135</td> <td>   79.581</td> <td> 0.000</td> <td>   10.448</td> <td>   10.976</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>post</th>                <td>    4.9807</td> <td>    0.134</td> <td>   37.074</td> <td> 0.000</td> <td>    4.717</td> <td>    5.244</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>post:C(region)[T.N]</th> <td>   -3.3458</td> <td>    0.183</td> <td>  -18.310</td> <td> 0.000</td> <td>   -3.704</td> <td>   -2.988</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>post:C(region)[T.S]</th> <td>   -4.9334</td> <td>    0.179</td> <td>  -27.489</td> <td> 0.000</td> <td>   -5.285</td> <td>   -4.582</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>post:C(region)[T.W]</th> <td>   -1.5408</td> <td>    0.179</td> <td>   -8.585</td> <td> 0.000</td> <td>   -1.893</td> <td>   -1.189</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>treated</th>             <td>    0.0503</td> <td>    0.117</td> <td>    0.429</td> <td> 0.668</td> <td>   -0.179</td> <td>    0.280</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>post:treated</th>        <td>    1.6811</td> <td>    0.156</td> <td>   10.758</td> <td> 0.000</td> <td>    1.375</td> <td>    1.987</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.table.SimpleTable'>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m = smf.ols('downloads ~ post*(treated + C(region))',\n",
    "            data=mkt_data_all).fit()\n",
    "\n",
    "m.summary().tables[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d4f0071",
   "metadata": {},
   "source": [
    "## Doubly Robust Diff-in-Diff\n",
    "\n",
    "### Propensity Score Model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "3b36415c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-01-20T14:39:16.905292Z",
     "start_time": "2024-01-20T14:39:16.903355Z"
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "9597c76f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-01-20T14:39:16.976538Z",
     "start_time": "2024-01-20T14:39:16.906798Z"
    }
   },
   "outputs": [],
   "source": [
    "unit_df = (mkt_data_all\n",
    "           # keep only the first date\n",
    "           .astype({\"date\": str})\n",
    "           .query(f\"date=='{mkt_data_all['date'].astype(str).min()}'\")\n",
    "           .drop(columns=[\"date\"])) # just to avoid confusion\n",
    "\n",
    "ps_model = smf.logit(\"treated~C(region)\", data=unit_df).fit(disp=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "156457a2",
   "metadata": {},
   "source": [
    "### Delta Outcome Model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "0461e333",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-01-20T14:39:16.983583Z",
     "start_time": "2024-01-20T14:39:16.977717Z"
    }
   },
   "outputs": [],
   "source": [
    "delta_y = (\n",
    "    mkt_data_all.query(\"post==1\").groupby(\"city\")[\"downloads\"].mean()\n",
    "    - mkt_data_all.query(\"post==0\").groupby(\"city\")[\"downloads\"].mean()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "b8062cbf",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-01-20T14:39:16.992595Z",
     "start_time": "2024-01-20T14:39:16.985055Z"
    }
   },
   "outputs": [],
   "source": [
    "df_delta_y = (unit_df\n",
    "              .set_index(\"city\")\n",
    "              .join(delta_y.rename(\"delta_y\")))\n",
    "\n",
    "outcome_model = smf.ols(\"delta_y ~ C(region)\", data=df_delta_y).fit()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42240030",
   "metadata": {},
   "source": [
    "### All Together Now\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "c444c816",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-01-20T14:39:17.005940Z",
     "start_time": "2024-01-20T14:39:16.994243Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>region</th>\n",
       "      <th>treated</th>\n",
       "      <th>tau</th>\n",
       "      <th>downloads</th>\n",
       "      <th>post</th>\n",
       "      <th>delta_y</th>\n",
       "      <th>y_hat</th>\n",
       "      <th>ps</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>W</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.087302</td>\n",
       "      <td>3.736539</td>\n",
       "      <td>0.176471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>N</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.436508</td>\n",
       "      <td>1.992570</td>\n",
       "      <td>0.212766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>W</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.761905</td>\n",
       "      <td>3.736539</td>\n",
       "      <td>0.176471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>W</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.396825</td>\n",
       "      <td>3.736539</td>\n",
       "      <td>0.176471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.476190</td>\n",
       "      <td>0.343915</td>\n",
       "      <td>0.176471</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     region  treated  tau  downloads  post   delta_y     y_hat        ps\n",
       "city                                                                    \n",
       "1         W        0  0.0       27.0     0  3.087302  3.736539  0.176471\n",
       "2         N        0  0.0       40.0     0  1.436508  1.992570  0.212766\n",
       "3         W        0  0.0       30.0     0  2.761905  3.736539  0.176471\n",
       "4         W        0  0.0       26.0     0  3.396825  3.736539  0.176471\n",
       "5         S        0  0.0       51.0     0 -0.476190  0.343915  0.176471"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dr = (df_delta_y\n",
    "         .assign(y_hat = lambda d: outcome_model.predict(d))\n",
    "         .assign(ps = lambda d: ps_model.predict(d)))\n",
    "\n",
    "df_dr.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "16ca3217",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-01-20T14:39:17.013829Z",
     "start_time": "2024-01-20T14:39:17.007363Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ATT: 1.6773180394442853\n"
     ]
    }
   ],
   "source": [
    "tr = df_dr.query(\"treated==1\")\n",
    "co = df_dr.query(\"treated==0\")\n",
    "\n",
    "dy1_treat = (tr[\"delta_y\"] - tr[\"y_hat\"]).mean()\n",
    "\n",
    "w_cont = co[\"ps\"]/(1-co[\"ps\"])\n",
    "dy0_treat = np.average(co[\"delta_y\"] - co[\"y_hat\"], weights=w_cont)\n",
    "\n",
    "print(\"ATT:\", dy1_treat - dy0_treat)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24548f97",
   "metadata": {},
   "source": [
    "## Staggered Adoption\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "8cf441b3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-01-20T14:39:17.038353Z",
     "start_time": "2024-01-20T14:39:17.015632Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>city</th>\n",
       "      <th>region</th>\n",
       "      <th>cohort</th>\n",
       "      <th>treated</th>\n",
       "      <th>tau</th>\n",
       "      <th>downloads</th>\n",
       "      <th>post</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-05-01</td>\n",
       "      <td>1</td>\n",
       "      <td>W</td>\n",
       "      <td>2021-06-20</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-05-02</td>\n",
       "      <td>1</td>\n",
       "      <td>W</td>\n",
       "      <td>2021-06-20</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-05-03</td>\n",
       "      <td>1</td>\n",
       "      <td>W</td>\n",
       "      <td>2021-06-20</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-05-04</td>\n",
       "      <td>1</td>\n",
       "      <td>W</td>\n",
       "      <td>2021-06-20</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-05-05</td>\n",
       "      <td>1</td>\n",
       "      <td>W</td>\n",
       "      <td>2021-06-20</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date  city region     cohort  treated  tau  downloads  post\n",
       "0 2021-05-01     1      W 2021-06-20        1  0.0       27.0     0\n",
       "1 2021-05-02     1      W 2021-06-20        1  0.0       28.0     0\n",
       "2 2021-05-03     1      W 2021-06-20        1  0.0       28.0     0\n",
       "3 2021-05-04     1      W 2021-06-20        1  0.0       26.0     0\n",
       "4 2021-05-05     1      W 2021-06-20        1  0.0       28.0     0"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mkt_data_cohorts = (pd.read_csv(\"./data/offline_mkt_staggered.csv\")\n",
    "                    .astype({\n",
    "                        \"date\":\"datetime64[ns]\",\n",
    "                        \"cohort\":\"datetime64[ns]\"}))\n",
    "\n",
    "mkt_data_cohorts.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "07878605",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-01-20T14:39:17.641301Z",
     "start_time": "2024-01-20T14:39:17.040228Z"
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt_data = (mkt_data_cohorts\n",
    "            .astype({\"date\":\"str\"})\n",
    "            .assign(treated_post = lambda d: d[\"treated\"]*(d[\"date\"]>=d[\"cohort\"]))\n",
    "            .pivot(\"city\", \"date\", \"treated_post\")\n",
    "            .reset_index()\n",
    "            .sort_values(list(sorted(mkt_data_cohorts.query(\"cohort!='2100-01-01'\")[\"cohort\"].astype(\"str\").unique())), ascending=False)\n",
    "            .reset_index()\n",
    "            .drop(columns=[\"city\"])\n",
    "            .rename(columns={\"index\":\"city\"})\n",
    "            .set_index(\"city\"))\n",
    "\n",
    "\n",
    "\n",
    "plt.figure(figsize=(16,8))\n",
    "\n",
    "sns.heatmap(plt_data, cmap=\"gray\",cbar=False)\n",
    "plt.text(18, 18, \"Cohort$=G_{05/15}$\", size=14)\n",
    "plt.text(38, 65, \"Cohort$=G_{06/04}$\", size=14)\n",
    "plt.text(55, 110, \"Cohort$=G_{06/20}$\", size=14)\n",
    "plt.text(35, 170, \"Cohort$=G_{\\\\infty}$\", color=\"white\", size=14, weight=3);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "0bf1dbe1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-01-20T14:39:17.650522Z",
     "start_time": "2024-01-20T14:39:17.642515Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>city</th>\n",
       "      <th>region</th>\n",
       "      <th>cohort</th>\n",
       "      <th>treated</th>\n",
       "      <th>tau</th>\n",
       "      <th>downloads</th>\n",
       "      <th>post</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-05-01</td>\n",
       "      <td>1</td>\n",
       "      <td>W</td>\n",
       "      <td>2021-06-20</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-05-02</td>\n",
       "      <td>1</td>\n",
       "      <td>W</td>\n",
       "      <td>2021-06-20</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-05-03</td>\n",
       "      <td>1</td>\n",
       "      <td>W</td>\n",
       "      <td>2021-06-20</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-05-04</td>\n",
       "      <td>1</td>\n",
       "      <td>W</td>\n",
       "      <td>2021-06-20</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-05-05</td>\n",
       "      <td>1</td>\n",
       "      <td>W</td>\n",
       "      <td>2021-06-20</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date  city region     cohort  treated  tau  downloads  post\n",
       "0 2021-05-01     1      W 2021-06-20        1  0.0       27.0     0\n",
       "1 2021-05-02     1      W 2021-06-20        1  0.0       28.0     0\n",
       "2 2021-05-03     1      W 2021-06-20        1  0.0       28.0     0\n",
       "3 2021-05-04     1      W 2021-06-20        1  0.0       26.0     0\n",
       "4 2021-05-05     1      W 2021-06-20        1  0.0       28.0     0"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mkt_data_cohorts_w = mkt_data_cohorts.query(\"region=='W'\")\n",
    "mkt_data_cohorts_w.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "45449b0d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-01-20T14:39:17.951595Z",
     "start_time": "2024-01-20T14:39:17.651779Z"
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(15, 10))\n",
    "\n",
    "plt_data = (mkt_data_cohorts_w\n",
    "            .groupby([\"date\", \"cohort\"])\n",
    "            [[\"downloads\"]]\n",
    "            .mean()\n",
    "            .reset_index()\n",
    ")\n",
    "\n",
    "\n",
    "\n",
    "for color, cohort in zip([\"C0\", \"C1\", \"C2\", \"C3\"], mkt_data_cohorts_w.query(\"cohort!='2100-01-01'\")[\"cohort\"].unique()):\n",
    "    df_cohort = plt_data.query(\"cohort==@cohort\")\n",
    "    sns.lineplot(data=df_cohort, x=\"date\", y=\"downloads\",\n",
    "                 label=pd.to_datetime(cohort).strftime('%Y-%m-%d'), ax=ax1)\n",
    "    ax1.vlines(x=cohort, ymin=25, ymax=50, color=color, ls=\"dotted\", lw=3)\n",
    "    \n",
    "    \n",
    "sns.lineplot(data=plt_data.query(\"cohort=='2100-01-01'\"), x=\"date\", y=\"downloads\", label=\"$\\infty$\", lw=4, ls=\"-.\", ax=ax1)\n",
    "        \n",
    "ax1.legend()\n",
    "ax1.set_title(\"Multiple Cohorts - West Region\");\n",
    "\n",
    "\n",
    "plt_data = (mkt_data_cohorts_w\n",
    "            .assign(days_to_treatment = lambda d: (pd.to_datetime(d[\"date\"])-pd.to_datetime(d[\"cohort\"])).dt.days)\n",
    "            .groupby([\"date\", \"cohort\"])\n",
    "            [[\"downloads\", \"days_to_treatment\"]]\n",
    "            .mean()\n",
    "            .reset_index()\n",
    ")\n",
    "\n",
    "\n",
    "for color, cohort in zip([\"C0\", \"C1\", \"C2\", \"C3\"], mkt_data_cohorts_w.query(\"cohort!='2100-01-01'\")[\"cohort\"].unique()):\n",
    "    df_cohort = plt_data.query(\"cohort==@cohort\")\n",
    "    sns.lineplot(data=df_cohort, x=\"days_to_treatment\", y=\"downloads\",\n",
    "                 label=pd.to_datetime(cohort).strftime('%Y-%m-%d'), ax=ax2)\n",
    "\n",
    "ax2.vlines(x=0, ymin=25, ymax=50, color=\"black\", ls=\"dotted\", lw=3)\n",
    "\n",
    "ax2.set_title(\"Multiple Cohorts (Aligned) - West Region\")\n",
    "ax2.legend();\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "2a4daaa8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-01-20T14:39:18.161933Z",
     "start_time": "2024-01-20T14:39:17.953112Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True Effect:  2.2625252108176266\n",
      "Estimated ATT: 1.7599504780633743\n"
     ]
    }
   ],
   "source": [
    "twfe_model = smf.ols(\n",
    "    \"downloads ~ treated:post + C(date) + C(city)\",\n",
    "    data=mkt_data_cohorts_w\n",
    ").fit()\n",
    "\n",
    "true_tau = mkt_data_cohorts_w.query(\"post==1&treated==1\")[\"tau\"].mean()\n",
    "\n",
    "print(\"True Effect: \", true_tau)\n",
    "print(\"Estimated ATT:\", twfe_model.params[\"treated:post\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "39a017b4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-01-20T14:39:18.623416Z",
     "start_time": "2024-01-20T14:39:18.163727Z"
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Late vs Early')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(15, 10), sharex=True)\n",
    "\n",
    "# fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 10))\n",
    "\n",
    "cohort_erly='2021-06-04'\n",
    "cohort_late='2021-06-20'\n",
    "\n",
    "## Early vs Late\n",
    "did_df = (mkt_data_cohorts_w\n",
    "            .loc[lambda d: d[\"date\"].astype(str) < cohort_late]\n",
    "            .query(f\"cohort=='{cohort_late}' | cohort=='{cohort_erly}'\")\n",
    "            .assign(treated = lambda d: (d[\"cohort\"] == cohort_erly)*1,\n",
    "                    post = lambda d: (d[\"date\"].astype(str) >= cohort_erly)*1))\n",
    "\n",
    "m = smf.ols(\n",
    "    \"downloads ~ treated:post + C(date) + C(city)\",\n",
    "    data=did_df\n",
    ").fit()\n",
    "\n",
    "\n",
    "# print(\"Estimated\", m.params[\"treated:post\"])\n",
    "# print(\"True\", did_df.query(\"post==1 & treated==1\")[\"tau\"].mean())\n",
    "\n",
    "plt_data = (did_df\n",
    "            .assign(installs_hat_0 = lambda d: m.predict(d.assign(treated=0)))\n",
    "            .groupby([\"date\", \"cohort\"])\n",
    "            [[\"downloads\", \"post\", \"treated\", \"installs_hat_0\"]]\n",
    "            .mean()\n",
    "            .reset_index())\n",
    "\n",
    "\n",
    "sns.lineplot(data=plt_data, x=\"date\", y=\"downloads\", hue=\"cohort\", ax=ax1)\n",
    "sns.lineplot(data=plt_data.query(\"treated==1 & post==1\"),\n",
    "             x=\"date\", y=\"installs_hat_0\", ax=ax1, ls=\"-.\", alpha=0.5, label=\"$\\hat{Y}_0|T=1$\")\n",
    "\n",
    "\n",
    "ax1.vlines(pd.to_datetime(cohort_erly), 26, 38, ls=\"dashed\")\n",
    "ax1.legend()\n",
    "ax1.set_title(\"Early vs Late\")\n",
    "\n",
    "\n",
    "# ## Late vs Early\n",
    "\n",
    "did_df = (mkt_data_cohorts_w\n",
    "            .loc[lambda d: d[\"date\"].astype(str) > cohort_erly]\n",
    "            .query(f\"cohort=='{cohort_late}' | cohort=='{cohort_erly}'\")\n",
    "            .assign(treated = lambda d: (d[\"cohort\"] == cohort_late)*1,\n",
    "                    post = lambda d: (d[\"date\"].astype(str) >= cohort_late)*1))\n",
    "\n",
    "m = smf.ols(\n",
    "    \"downloads ~ treated*post + C(date) + C(city)\",\n",
    "    data=did_df\n",
    ").fit()\n",
    "\n",
    "# print(\"Estimated\", m.params[\"treated:post\"])\n",
    "# print(\"True\", did_df.query(\"post==1 & treated==1\")[\"tau\"].mean())\n",
    "\n",
    "\n",
    "plt_data = (did_df\n",
    "            .assign(installs_hat_0 = lambda d: m.predict(d.assign(treated=0)))\n",
    "            .groupby([\"date\", \"cohort\"])\n",
    "            [[\"downloads\", \"post\", \"treated\", \"installs_hat_0\"]]\n",
    "            .mean()\n",
    "            .reset_index())\n",
    "\n",
    "\n",
    "sns.lineplot(data=plt_data, x=\"date\", y=\"downloads\", hue=\"cohort\", ax=ax2)\n",
    "sns.lineplot(data=plt_data.query(\"treated==1 & post==1\"),\n",
    "             x=\"date\", y=\"installs_hat_0\", ax=ax2, ls=\"-.\", alpha=0.5, label=\"$\\hat{Y}_0|T=1$\")\n",
    "\n",
    "ax2.vlines(pd.to_datetime(\"2021-06-20\"), 32, 45, ls=\"dashed\")\n",
    "ax2.legend()\n",
    "ax2.set_title(\"Late vs Early\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0221c59e",
   "metadata": {},
   "source": [
    "### Heterogeneous Effect over Time\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "63a48d15",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-01-20T14:39:19.558367Z",
     "start_time": "2024-01-20T14:39:18.624891Z"
    }
   },
   "outputs": [],
   "source": [
    "formula = \"downloads ~ treated:post:C(cohort):C(date) + C(city)+C(date)\"\n",
    "\n",
    "twfe_model = smf.ols(formula, data=mkt_data_cohorts_w).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "7a037a3e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-01-20T14:39:19.603935Z",
     "start_time": "2024-01-20T14:39:19.559718Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of param.: 510\n",
      "True Effect:  2.2625252108176266\n",
      "Pred. Effect:  2.259766144685074\n"
     ]
    }
   ],
   "source": [
    "df_pred = (\n",
    "    mkt_data_cohorts_w\n",
    "    .query(\"post==1 & treated==1\")\n",
    "    .assign(y_hat_0=lambda d: twfe_model.predict(d.assign(treated=0)))\n",
    "    .assign(effect_hat=lambda d: d[\"downloads\"] - d[\"y_hat_0\"])\n",
    ")\n",
    "\n",
    "print(\"Number of param.:\", len(twfe_model.params))\n",
    "print(\"True Effect: \", df_pred[\"tau\"].mean())\n",
    "print(\"Pred. Effect: \", df_pred[\"effect_hat\"].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "08d5e8fd",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-01-20T14:39:20.575551Z",
     "start_time": "2024-01-20T14:39:19.605555Z"
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f9610786a90>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "formula = \"downloads ~ treated:post:C(cohort):C(date) + C(city) + C(date)\"\n",
    "\n",
    "twfe_model = smf.ols(formula, data=mkt_data_cohorts_w.astype({\"date\":str, \"cohort\": str})).fit()\n",
    "\n",
    "effects = (twfe_model.params[twfe_model.params.index.str.contains(\"treated\")]\n",
    "           .reset_index()\n",
    "           .rename(columns={0:\"param\"})\n",
    "           .assign(cohort=lambda d: d[\"index\"].str.extract(r'C\\(cohort\\)\\[(.*)\\]:'))\n",
    "           .assign(date=lambda d: d[\"index\"].str.extract(r':C\\(date\\)\\[(.*)\\]'))\n",
    "           .assign(date=lambda d: pd.to_datetime(d[\"date\"]), cohort=lambda d: pd.to_datetime(d[\"cohort\"])))\n",
    "\n",
    "plt.figure(figsize=(10,4))\n",
    "sns.lineplot(data=effects, x=\"date\", y=\"param\", hue=\"cohort\", palette=\"gray\")\n",
    "plt.xticks(rotation=45)\n",
    "plt.ylabel(\"Estimated Effect\")\n",
    "plt.legend(fontsize=12)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "f7cb29c6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-01-20T14:39:20.612808Z",
     "start_time": "2024-01-20T14:39:20.576937Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>att_g</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.455535</td>\n",
       "      <td>702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.659068</td>\n",
       "      <td>1044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.573687</td>\n",
       "      <td>420</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      att_g  size\n",
       "0  3.455535   702\n",
       "1  1.659068  1044\n",
       "2  1.573687   420"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cohorts = sorted(mkt_data_cohorts_w[\"cohort\"].unique())\n",
    "\n",
    "treated_G = cohorts[:-1]\n",
    "nvr_treated = cohorts[-1]\n",
    "\n",
    "def did_g_vs_nvr_treated(df: pd.DataFrame,\n",
    "                         cohort: str,\n",
    "                         nvr_treated: str,\n",
    "                         cohort_col: str = \"cohort\",\n",
    "                         date_col: str = \"date\",\n",
    "                         y_col: str = \"downloads\"):\n",
    "    did_g = (\n",
    "        df\n",
    "        .loc[lambda d:(d[cohort_col] == cohort)|\n",
    "                      (d[cohort_col] == nvr_treated)]\n",
    "        .assign(treated = lambda d: (d[cohort_col] == cohort)*1)\n",
    "        .assign(post = lambda d:(pd.to_datetime(d[date_col])>=cohort)*1)\n",
    "    )\n",
    "    \n",
    "    att_g = smf.ols(f\"{y_col} ~ treated*post\",\n",
    "                    data=did_g).fit().params[\"treated:post\"]\n",
    "    size = len(did_g.query(\"treated==1 & post==1\"))\n",
    "    return {\"att_g\": att_g, \"size\": size}\n",
    "\n",
    "\n",
    "atts = pd.DataFrame(\n",
    "    [did_g_vs_nvr_treated(mkt_data_cohorts_w, cohort, nvr_treated)\n",
    "     for cohort in treated_G]\n",
    ")\n",
    "    \n",
    "atts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "6be01a88",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-01-20T14:39:20.618637Z",
     "start_time": "2024-01-20T14:39:20.614787Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.2247467740558697"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(atts[\"att_g\"]*atts[\"size\"]).sum()/atts[\"size\"].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c39f917a",
   "metadata": {},
   "source": [
    "### Covariates\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "a0db0197",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-01-20T14:39:26.293019Z",
     "start_time": "2024-01-20T14:39:20.620396Z"
    }
   },
   "outputs": [],
   "source": [
    "formula = \"\"\"\n",
    "downloads ~ treated:post:C(cohort):C(date)\n",
    "+ C(date):C(region) + C(city) + C(date)\"\"\"\n",
    "\n",
    "twfe_model = smf.ols(formula, data=mkt_data_cohorts).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "91ba286e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-01-20T14:39:26.472928Z",
     "start_time": "2024-01-20T14:39:26.294479Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of param.: 935\n",
      "True Effect:  2.078397729895905\n",
      "Pred. Effect:  2.0426262863584568\n"
     ]
    }
   ],
   "source": [
    "df_pred = (\n",
    "    mkt_data_cohorts\n",
    "    .query(\"post==1 & treated==1\")\n",
    "    .assign(y_hat_0=lambda d: twfe_model.predict(d.assign(treated=0)))\n",
    "    .assign(effect_hat=lambda d: d[\"downloads\"] - d[\"y_hat_0\"])\n",
    ")\n",
    "\n",
    "print(\"Number of param.:\", len(twfe_model.params))\n",
    "print(\"True Effect: \",  df_pred[\"tau\"].mean())\n",
    "print(\"Pred. Effect: \", df_pred[\"effect_hat\"].mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38a89222",
   "metadata": {},
   "source": [
    "## Key Ideas\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "32a42086",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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